Revisiting Success in Music Streaming: A Data-Driven Predictive Approach

Juan D Montoro-Pons & Manuel Cuadrado-García

Universitat de València

María Luisa Palma-Martos

Universidad de Sevilla

Highlights

Incorporate cultural practices (via unstructured data sources) into empirical models to enrich the representation of actors in the cultural sectors
Include non-parametric/flexible statistical learning models in the toolbox of quantitative cultural economics
Assess empirical models based on out-of-sample performance providing robustness to findings (does the model perform on unseen data?)
Use of alternative tools to empirically interpret the contribution of track/artist-related features into chart performace

Ultimate goal is to identify what features have an impact on success in the streaming economy and learn causal effects using flexible machine learning tools

Commercial success in the streaming economy

Performance in streaming charts has been explored extensively from a managerial/economic standpoint as well as through broader interdisciplinary lenses. Survival in charts has been found to be associated to being signed to a major label and level of competition (Kaimann, Tanneberg, and Cox 2021), the influence of media exposure and electronic word of mouth (Lee and Kim 2024), or early adoption of new genres/subgenres (Sobchuk, Youngblood, and Morin 2024).

Individual sonic features of songs (both objective and perceptual) have been proposed as predictors of success: Askin and Mauskapf (2017) introduce a metric of tipicality of a song based on its features (and suggest a similarity/differentiation tradeoff). (See also Interiano et al. 2018; Saragih 2023)

Artists’ collaborations has been a specific topic to explain streaming performance: the impact of joint efforts on commercial outcomes or the benefits of increasing the disimilarity between artists in collaborations have been both analyzed from a causal standpoint (Ordanini, Nunes, and Nanni 2018; McKenzie, Crosby, and Lenten 2021).

Collaboration networks allow researchers to use artist association topologies as predictors (Kang, Mandulak, and Szymanski 2022) and identify popularity bias in centrality measures , i.e., how fame skews network importance metrics (South, Roughan, and Mitchell 2020).

The dataset

The primary dataset is retrieved from Spotify’s global weekly chart, covering the period from 29/09/2013 to 23/01/2025.
The sampling unit is a track (song)
Using webscraping and the Spotify API we collect information about a track’s success (peak position on charts, weeks at peak position, maximum weekly streams, total streams, and a popularity index) and a set of track and artist features including information about genres, type of album, release date, artist(s) popularity and followers, whether the track is a collaboration, markets in which the album is present, and audio features of the track to mention some. Features can be classified as
- Track-specific
- Album-related
- Artist(s)-related (e.g., online tags from LastFM and MusicBrainz)
The dataset includes information on 4153 tracks by 1670 unique artists.

Descriptive analysis

Describing the dataset: tracks

variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
weeks	0	1.000	18.745	33.810	1.000	1.000	6.000	22.000	407.000	▇▁▁▁▁
top_ten	3680	0.114	8.951	8.917	1.000	2.000	6.000	14.000	64.000	▇▂▁▁▁
streams	0	1.000	155.566	337.316	0.174	8.262	32.860	146.228	4604.759	▇▁▁▁▁
peek_position	0	1.000	80.513	59.874	1.000	27.000	69.000	127.000	240.000	▇▅▃▃▁
track_popularity	0	1.000	54.469	23.105	0.000	50.000	60.000	69.000	90.000	▂▁▃▇▃
duration_s	0	1.000	208.258	49.196	35.240	178.107	203.641	230.953	613.027	▁▇▁▁▁
loudness	0	1.000	-6.305	2.491	-34.475	-7.497	-5.899	-4.663	1.509	▁▁▁▇▇
acousticness	0	1.000	0.216	0.235	0.000	0.037	0.125	0.320	0.994	▇▂▁▁▁
instrumentalness	0	1.000	0.013	0.075	0.000	0.000	0.000	0.000	0.927	▇▁▁▁▁
speechiness	0	1.000	0.126	0.118	0.023	0.045	0.074	0.172	0.966	▇▂▁▁▁
danceability	0	1.000	0.681	0.142	0.150	0.590	0.695	0.786	0.985	▁▂▅▇▃
energy	0	1.000	0.642	0.166	0.028	0.537	0.657	0.766	0.989	▁▂▆▇▃
valence	0	1.000	0.484	0.222	0.032	0.313	0.481	0.656	0.976	▃▇▇▆▃

Describing the dataset: artists and collaborations

The dataset includes information on 1670 unique artists.
Of all the tracks, 45% are collaborations between artists while 55% are solo tracks
Artist roles is split between:
- solo (34% of occurrences)
- lead (27%)
- feature (39%).
Most frequent collaborations are between two artists: mean, median and Q1 of number of collaborators are 2.89, 2 and 3 respectively.

artist_name	tracks	collab_ratio
Drake	172	61%
Bad Bunny	110	57.3%
Taylor Swift	98	13.3%
Travis Scott	84	73.8%
Future	73	76.7%
Ariana Grande	66	40.9%
21 Savage	63	76.2%
The Weeknd	61	44.3%
Ed Sheeran	56	50%
Lil Baby	56	78.6%

Describing the dataset: more on collaborations

Spotify API produces an index of popularity for artists. Furthermore it provides the number of followers in the platform.
For each collaboration we compute:
- The joint popularity
- The popularity_ratio of each artist
- The joint number of followers
- The followers_ratio of each artist

measure	Role: feature	Role: lead
% of artists that are more popular	0.46	0.54
% of artists that have more followers	0.39	0.55
Average popularity ratio	0.99	1.02
Average followers ratio	0.89	1.17
Median popularity ratio	0.99	1.01
Median followers ratio	0.71	1.12

Outcome	Collab FALSE	Collab TRUE
popularity_track	55.61	53.28
weeks_in_lists	19.60	17.11
top_ten	8.60	9.17
peek	80.07	82.14
times_peek	3.19	3.69
streams	163.56	139.72

Engineering rich artist representations

Collaboration networks

Degree centrality
Betweenness centrality
Centrality measures

	label	degree	eigen	closeness	betweeness
3TVXtAsR1Inumwj472S9r4	Drake	132	1.0000000	0.0003401	31930.107
0Y5tJX1MQlPlqiwlOH1tJY	Travis Scott	109	0.7049464	0.0003357	17987.294
4q3ewBCX7sLwd24euuV69X	Bad Bunny	93	0.0559605	0.0003233	35985.381
1vyhD5VmyZ7KMfW5gqLgo5	J Balvin	84	0.0500565	0.0003630	53044.032
2R21vXR83lH98kGeO99Y66	Anuel AA	81	0.0346626	0.0003212	20480.496
1RyvyyTE3xzB2ZywiAwp0i	Future	79	0.7645781	0.0003185	14613.223
50co4Is1HCEo8bhOyUWKpn	Young Thug	78	0.6576292	0.0003368	11283.802
1i8SpTcr7yvPOmcqrbnVXY	Ozuna	76	0.0321060	0.0003227	14830.585
77ziqFxp5gaInVrF2lj4ht	Sech	73	0.0294024	0.0002987	3370.446
1URnnhqYAYcrqrcwql10ft	21 Savage	67	0.7061541	0.0003232	8718.683
5f7VJjfbwm532GiveGC0ZK	Lil Baby	66	0.4302981	0.0003224	13987.667
1pQWsZQehhS4wavwh7Fnxd	Lenny Tavárez	62	0.0211659	0.0002734	1332.630
0KPX4Ucy9dk82uj4GpKesn	Dalex	61	0.0206827	0.0002710	1275.171
1SupJlEpv7RS2tPNRaHViT	Nicky Jam	61	0.0256472	0.0003006	6835.123
329e4yvIujISKGKz1BZZbO	Farruko	61	0.0247795	0.0003042	10499.564

Louvain algorithm

The Louvain algorithm is a (greedy) method to detect non-overlapping communities in a graph where the value to be optimized is modularity, defined as a value in the range [−1,1] that measures the density of links inside communities compared to links between communities.

Two stages method:

First, small communities are found by optimizing modularity locally on all nodes. This process is applied repeatedly and sequentially to all nodes until no modularity increase can occur.

Then each small community is grouped into one node and the first step is repeated. For each community in our graph’s partition, the individual nodes making up that community are combined and the community itself becomes a node. The edges connecting distinct communities are used to weight the new edges connecting our aggregate nodes. From here, the process can be repeated so that more nodes are moved into existing communities until an optimal level of modularity is reached.

Centrality measures

Degree Centrality: The degree of a node is the number of other nodes that single node is connected to. Important nodes tend to have more connections to other nodes. Highly connected nodes are interpreted to have high degree centrality.
Eigenvector Centrality: The extent to which adjacent nodes are connected themselves also indicate importance (e.g., Important nodes increase the importance of other nodes).
Closeness centrality: Closeness centrality measures how many steps are required to access every other node from a given node. In other words, important nodes have easy access to other nodes given multiple connections.
Betweenness Centrality: This ranks the nodes based on the flow of connections through the network. Importance is demonstrated through high frequency of connection with multiple other nodes. Nodes with high levels of betweenness tend to serve as a bridge for multiple sets of other important nodes.

Tags: `LastFM`

Tags: `MusicBrainz`

Tag mapping

Started with a dataset containing a list of music artists.
Tag collection via LastFM API
- Retrieved user-generated tags for each artist using the LastFM API.
- Tags reflect how listeners perceive and describe each artist (genre-related such as “rock”, “indie”, “electronic”, but also unrelated such as “best of 2024”, “seen live”, or “worst ive heard today”).
Building the artist-tag matrix
- Constructed a Document-Term Matrix (DTM):
  - A DTM is a way to represent text data (or in this case, tag data) numerically
  - Think of it as a large table: Rows represent artists; Columns represent tags.
  - Each cell shows how strongly a tag is associated with an artist (e.g., frequency or weight).
- Once we have the DTM, we can compute similarity between artists:
- Cosine Similarity: Measures how similar two tag vectors are, regardless of their magnitude.
- Other transformations (e.g., TF-IDF) can refine the matrix for better comparisons.

Cosine similarity: example

DTM engineered features

Pairwise similarity betwen artists (i.e., tag vectors) in a collaboration (specific for tracks that are collaborations).
Mean similarity to other artists: how generally “close” a tag vector is to the rest.
Similarity entropy of an artist with all other artists: measures how evenly similar artist \(i\) is to all other artists, how “focused” or “distributed” the similarity of a tag vector is. High entropy means an artist is similarly related to many artists (a genre-blender or mainstream artist). Low entropy means an artist is strongly similar to only a few artists (niche or highly distinctive artist).
Distance of a tag vector to centroid (cosine-based): interpret each artist as a vector in the similarity space and compute its distance to the centroid of all artists (Generic, central, similar to many others vs. distinctive, specific, potentially outlier).

To sum up

Variable	Description	Type
`track_popularity`	Popularity score of the track (0–100) from Spotify	Y
`top_ten`	Indicator whether the track reached Top 10	Y
`peek_position`	Highest chart position reached	Y
`times_at_peek`	Number of times the track was at its peak position	Y
`peek_streams`	Number of streams during the peak week	Y
`streams`	Total number of streams	Y
`track_id`	Unique identifier of the track	X
`explicit`	Whether the track contains explicit content	X
`album_type`	Type of album (e.g., single, album)	X
`release_date`	Date when the track was released	X
`disc_number`	Disc number (in case of multi-disc albums)	X
`total_tracks`	Total number of tracks in the album	X
`track_number`	Position of the track within the album	X
`number_markets`	Number of markets where the track is available	X
`markets`	List of countries where the track is available	X
`collaboration`	Whether the track is a collaboration	X
`number_collaborators`	Number of artists involved in the track	X
`role`	Role of the artist (lead, feature, solo)	X
`artist_genres`	List of genres associated with the artist	X
`followers`	Number of followers the artist has	X
`artist_popularity`	Popularity score of the artist (0–100) from Spotify	X
`weeks`	Number of weeks in the ranking or tracking period	X
`time_signature`	Musical time signature (e.g., 4/4, 3/4)	X
`track_name`	Name of the track	X
`duration_ms`	Duration of the track in milliseconds	X
`danceability`	How suitable a track is for dancing (0–1)	X
`energy`	Energy level of the track (0–1)	X
`key`	Key of the song (0=C, 1=C#/Db, …, 11=B)	X
`loudness`	Average loudness in decibels	X
`mode`	Musical mode: major (1) or minor (0)	X
`speechiness`	Degree of spoken words in the track (0–1)	X
`acousticness`	Confidence that the track is acoustic (0–1)	X
`instrumentalness`	Likelihood that the track has no vocals (0–1)	X
`liveness`	Probability the track was recorded live (0–1)	X
`valence`	Musical positiveness or mood (0–1)	X
`tempo`	Beats per minute (BPM) of the track	X
`year`	Release year	X
`month`	Release month	X
`ratio_pop`	`artist_popularity` / if collab `sum_popularity_artists_in_collab` otherwise 1	X
`ratio_followers`	`followers` /if collab `sum_followers_artists_in_collab` otherwise 1	X
`more_popular`	Logical: `artist_popularity` > `mean_collab_popularity`	X
`more_followers`	Logical: `followers` > `mean_collab_followers`	X
`centrality_measures`	graph-related centrality measures	X
`distance_measures`	DTM-related centrality measures	X

Predicting success with classifiers

Modeling strategy

We choose three different categorical responses. Specifically:
- track is in the top_ten
- track is in the first quartile of streams distribution
- track is in the top 50% of the streams distribution
We fit different statistical learning models to the dataset. These are:
- Logistic regression (lr, with and without regularizacion)
- Two ensembles: a random forest (rf) and an extreme gradient boosting classifier (xgb)
- Two support vector machines (linear and non-linear): lsvc and svc
- Neural network (nn)
Performance of all models is assessed using a holdout sample through 5-fold cross-validation
Regularization parameters are selected via cross-validation (using the f1 score as the metric of predictive performance)

Best models for each response will be further analyzed

Estimation results

Top ten
Top quartile
Top 50%

Model	mean_test_accuracy	mean_test_f1	mean_test_recall	mean_test_precision
lr	0.626	0.270	0.600	0.175
rf	0.819	0.326	0.389	0.285
xgb	0.885	0.138	0.081	0.526
xgb2	0.788	0.316	0.437	0.251
svc	0.744	0.304	0.496	0.221
lsvc	0.649	0.289	0.631	0.188
nn	0.885	0.020	0.010	0.233

Model	mean_test_accuracy	mean_test_f1	mean_test_recall	mean_test_precision
lr	0.573	0.420	0.613	0.322
rf	0.620	0.450	0.623	0.353
xgb	0.737	0.287	0.213	0.447
xgb2	0.559	0.453	0.732	0.328
svc	0.606	0.457	0.666	0.349
lsvc	0.624	0.443	0.599	0.352
nn	0.738	0.242	0.171	0.431

Model	mean_test_accuracy	mean_test_f1	mean_test_recall	mean_test_precision
lr	0.607	0.608	0.612	0.606
rf	0.669	0.679	0.700	0.660
xgb	0.664	0.669	0.678	0.660
xgb2	0.624	0.700	0.880	0.582
svc	0.659	0.665	0.678	0.654
lsvc	0.633	0.629	0.622	0.636
nn	0.634	0.653	0.691	0.624

Interpretability of black-box models

Shapley values

Measure each variable’s individual contribution to a model’s prediction.
Shapley values produce local explanations (individual predictions) but can be interpreted globally
The working: For each prediction of the model:
1. Consider a baseline prediction (the average)
2. For every variable \(i\) and every possible combination of features \(J\), \(i \not\in J\)
- Obtain the prediction of the model including features \(J\) with and without feature \(i\): the difference between these two predictions is the contribution of \(i\)
- Average the contributions computed in the previous step (contribution of \(i\) for all possible subsets \(J\))
The method relies on Monte Carlo sampling to approximate Shapley values by randomly sampling subsets of features rather than evaluating them all.

Shapley values are a method from cooperative game theory that allows us to measure each feature’s individual contribution to a model’s prediction. Think of the model’s prediction as a “reward” that needs to be fairly distributed among “players” (the features). By calculating Shapley values, we determine how much each feature contributed to the prediction by seeing how the prediction changes when we add or remove that feature from various subsets of features.
To calculate Shapley values, we use a value function representing the model’s prediction based on any subset of features. This function lets us measure how much the prediction changes when a particular feature is added to a subset, capturing the marginal contribution of each feature.
Because the model’s prediction depends on all features—even those not in the subset we’re focusing on—we use marginalization to account for the remaining features by averaging their possible values. This lets us isolate the specific impact of the features in our subset.
The Shapley value for a feature is computed by averaging its marginal contributions across all possible subsets of features. This ensures that each feature’s contribution is fairly represented. However, calculating Shapley values for all subsets becomes computationally intense as the number of features increases since there are exponentially many subsets to consider.
To simplify this, we use Monte Carlo sampling to approximate Shapley values by randomly sampling subsets of features rather than evaluating them all. This method involves comparing the model’s predictions on random subsets with and without a specific feature and then averaging these differences across samples of records to estimate the feature’s contribution.
When estimating Shapley values through sampling, it’s important to evaluate how close these approximations are to the true values. Hoeffding’s inequality helps us by providing a statistical guarantee: it bounds the probability that the sampled-based Shapley values will deviate from the true Shapley values by more than a specified amount.

\[P\left(|\hat{\phi_j} - \phi_j | \right) \leq 2 \exp\left( -\frac{2 K \epsilon ^2}{(b-a)^2}\right)\]

with \(\hat{\phi_j}\) and \(\phi_j\) predicted and actual Shapley value, \(\epsilon\) the acceptable error margin, \(K\) the number of samples and \(b\) and \(a\) represent the range within which the model’s output varies for a single prediction. This shows that as we increase \(K\) the estimate becomes more reliable.

By applying Hoeffding’s inequality, we can establish that, for a sufficiently large number of samples \(K\), the probability that our Shapley value estimate deviates from the actual value by more than a small error margin \(\epsilon\) becomes very small.
KernelExplainer estimates Shapley values by sampling subsets and comparing predictions with and without each feature. Although KernelExplainer is flexible, it assumes feature independence, which can lead to slight biases when features are correlated: it uses marginal sampling rather than conditional sampling and consequently, the Shapley values calculated with KernelExplainer may be biased by its assumption that features are independent. Given our experiment’s natural grouping of features, Owen values may have been a more suitable choice from the start, as they can account for feature interactions.
Like Shapley values, Owen values aim to fairly distribute the model’s prediction among features. However, instead of evaluating features individually, Owen values allow us to create coalitions of features that act together.
To calculate Owen values:
- We first form coalitions based on related features.
- Then, we treat each coalition as a single “player” in the cooperative game, calculating the Shapley value for each coalition.
- Finally, within each coalition, we distribute the coalition’s Shapley value among individual features based on their individual contributions within the coalition.
This two-step process (calculating Shapley values for coalitions, then distributing within each coalition) ensures that Owen values accurately reflect both individual and collective contributions, especially when features are interdependent.
In machine learning explainability, choosing between Shapley and Owen values is a strategic decision. Shapley values excel in analyzing independent contributions, while Owen values are better suited for capturing interactions within feature groups. This distinction is crucial when dependencies exist, as Owen values provide a more accurate reflection of joint influences.
When features are correlated, the use of KernelExplainer from the SHAP library can lead to biased results, since this method assumes all features are independent. In cases where features naturally form groups, Owen values offer a better alternative by accurately capturing feature interactions through grouped coalitions. Combining Shapley and Owen values is beneficial in complex models with independent and interdependent features.

Shapley values: top ten

Local explanation (i)
Local explanation (ii)
Global explanation (i)
Global explanation (ii)

Shapley values: top quartile

Shapley values: top 50%

Summary: impact of individual variables on model predictions

Feature	Top_ten	Top_25	Top_50
Entropy	(+)	(+)	(+-)
Centroid similarity	(+)	(-)	(-)
Mean similarity	(+)	(+)	(+)
Betweenness	(+)	(+)	(+-)
Eigenvalue centrality	(+-)	(+-)	(+-)
Closeness	(+-)	(+-)	(+-)
Similarity (pairwise)	(-)	(+-)	(+-)
Popularity ratio	(+-)	(-)	(+-)
Followers ratio	(-)	(-)	(+-)
Track characteristics	Pop(+), hip-hop(-), duration (+), energy (-)	Speechiness (-), energy(-), danceability(+), explicit (-), number collaborators (-)	Explicit (-), loudness (+), danceability (+), speechiness (-), energy (-), duration(+-)

Discussion

Findings

The work has generated insights about success in streaming music. Particularly:

We refined the representation of artists using two (unstructured) data sources:
- As nodes in a graph allowing to incorporate metrics of connections between them
- As mappings to user-defined labels that allows to introduce measures of similarity/distance that go beyond curated lists of genres (that might convey different meaning to different users)
Based on the Shapley values for the best models it is found that:
- Graph centrality metrics are weakly associated to charts performance, particularly betweenness, which measures the function of nodes (artists) that facilitate the flow of information within the network
- Similarity-derived metrics are consistently strongly associated to the outcome: in most cases, high values are related to success. Higher values for entropy, centroid or mean similarity increase predicted probability of success
- Regarding collaborations, we find some evidence of pairwise similarity affecting success negatively: the more different two artists collaborating the more the probability of success
- Furthermore there is some evidence of the asymmetric benefits of collaborations: being lead in an asymmetric collaboration (having less followers than the featured artist) is related to a greater probability of success.

Concluding remarks

Incorporating flexible (non-parametric) approaches to predict sucess in the streaming economy brought about gains in models performance
The integration of unstructured data into quantitative research broadens the methodological toolkit of researchers in cultural economics, allowing for a richer and more nuanced representation of complex problems
Of course, predictive performance and empirical associations are not causal relations. However, these can be seen as a first step to estimate structural coefficients using machine learning techniques:
- Double/debiased machine learning
- Metalearners for heterogeneous treatment effects (HTEs)
- Other techniques for HTEs (e.g.causal forests)

Thanks!

Appendix: models excluding network metrics

Training results

Top ten
Top quartile
Top 50%

Model	mean_test_accuracy	mean_test_f1	mean_test_recall	mean_test_precision
lr	0.613	0.241	0.539	0.156
rf	0.767	0.302	0.446	0.229
xgb	0.880	0.089	0.051	0.340
xgb2	0.724	0.295	0.511	0.209
svc	0.690	0.258	0.479	0.178
lsvc	0.627	0.263	0.589	0.170
nn	0.881	0.018	0.010	0.071

Model	mean_test_accuracy	mean_test_f1	mean_test_recall	mean_test_precision
lr	0.569	0.418	0.615	0.318
rf	0.657	0.448	0.560	0.375
xgb	0.733	0.267	0.195	0.427
xgb2	0.593	0.447	0.663	0.338
svc	0.627	0.450	0.611	0.356
lsvc	0.621	0.441	0.600	0.350
nn	0.737	0.154	0.103	0.431

Model	mean_test_accuracy	mean_test_f1	mean_test_recall	mean_test_precision
lr	0.569	0.418	0.615	0.318
rf	0.657	0.448	0.560	0.375
xgb	0.733	0.267	0.195	0.427
xgb2	0.593	0.447	0.663	0.338
svc	0.627	0.450	0.611	0.356
lsvc	0.621	0.441	0.600	0.350
nn	0.737	0.154	0.103	0.431

Shapley values

Top 10
Top quartile
Top 50%

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Revisiting Success in Music Streaming: A Data-Driven Predictive Approach

Juan D Montoro-Pons & Manuel Cuadrado-García

Universitat de València

María Luisa Palma-Martos

Universidad de Sevilla

Highlights

Commercial success in the streaming economy

The dataset

Descriptive analysis

Describing the dataset: tracks

Describing the dataset: artists and collaborations

Describing the dataset: more on collaborations

Asymmetric collaborations

Engineering rich artist representations

Creating new artist-related variables

Collaboration networks

Tags: LastFM

Tags: MusicBrainz

Tag mapping

Cosine similarity: example

DTM engineered features

To sum up

Predicting success with classifiers

Modeling strategy

Estimation results

Interpretability of black-box models

Shapley values

Shapley values: top ten

Shapley values: top quartile

Shapley values: top 50%

Summary: impact of individual variables on model predictions

Discussion

Findings

Concluding remarks

Thanks!

Appendix: models excluding network metrics

Training results

Shapley values

References

Tags: `LastFM`

Tags: `MusicBrainz`